AI Integration &
Development
What We Build
End-to-end AI engineering from integration to custom model development — all production-ready.
Large Language Model
Capabilities
Our ML engineers cover the full LLM lifecycle — from base model selection and fine-tuning to production serving and continuous optimization.
Model Selection & Architecture
We evaluate and select the right base model (open-source or proprietary) based on your latency, cost, privacy, and accuracy requirements — from Llama 3 to GPT-4o to Mixtral.
Fine-Tuning & Domain Adaptation
Using LoRA, QLoRA, and RLHF techniques, we adapt foundation models to your specific domain, vocabulary, tone, and compliance requirements for dramatically improved task performance.
RAG & Knowledge Grounding
Implement Retrieval-Augmented Generation with vector databases (Pinecone, Weaviate, pgvector) to give your LLMs accurate, up-to-date enterprise knowledge without hallucination.
Model Training & Pre-Training
For organizations requiring full model ownership, we manage end-to-end pre-training pipelines on proprietary data — including data curation, tokenizer design, and distributed training.
LLM Evaluation & Red-Teaming
Rigorous evaluation frameworks using RAGAS, ROUGE, BERTScore, and custom benchmarks to measure hallucination, factuality, and safety before production deployment.
MLOps & Production Serving
We deploy models using vLLM, TGI, or Triton Inference Server for high-throughput, low-latency serving at scale. Complete with CI/CD, retraining triggers, and drift monitoring.
From Raw Data to Intelligent Responses
From Automation to Autonomy
We build agentic AI systems that plan, reason, and act — transforming complex, multi-step business processes into self-executing intelligent workflows.
Multi-Agent Orchestration
Deploy collaborative agent networks using LangGraph and AutoGen where specialized AI agents delegate, execute, and verify tasks in parallel.
Tool-Calling & API Integration
LLMs that autonomously call APIs, query databases, browse the web, and execute code — turning language into real-world action.
Continuous Learning Loops
AI systems that improve from feedback, usage patterns, and new data — ensuring long-term accuracy and relevance without manual retraining cycles.
Delivery Lifecycle
From Concept to Production-Grade AI
Our structured approach ensures AI initiatives move from experimental prototypes to mission-critical, self-improving operational tools with zero friction.
Discovery & Data Audit
Mapping your data landscape, identifying AI opportunities, and selecting the right model architecture for your use case.
Model Design & Fine-Tuning
Custom neural architectures, domain-specific LLM fine-tuning (LoRA/RLHF), and RAG knowledge-grounding.
Evaluation & Red-Teaming
Rigorous benchmarking, hallucination testing, and adversarial validation before any production deployment.
MLOps & Scale
Automated retraining pipelines, high-throughput serving (vLLM/TGI), drift monitoring, and continuous ROI optimization.
The Infrastructure of Intelligence
Engineered with the World's Most Advanced AI Frameworks
AI Solutions Across Every Sector
AI Implementation Intelligence
Questions & Answers
Fine-tuning bakes domain knowledge into model weights for consistent tone, style, and task performance — ideal for classification, extraction, and branded content. RAG (Retrieval-Augmented Generation) dynamically fetches up-to-date information from your knowledge base at inference time — ideal for customer Q&A, compliance, and support. At Abrus, we combine both techniques for enterprise deployments requiring both accuracy and freshness.
We use VPC-isolated environments and PII-stripping pipelines before any data reaches an inference engine. For regulated industries (healthcare, banking, government), we deploy quantized open-source models (Llama 3, Mistral) on your private infrastructure — ensuring zero data leakage to third-party APIs. All deployments are architected with SOC-2 and ISO 27001 compliance in mind.
Yes. Our agentic systems use tool-calling and API integration at the core. We've connected agents to Salesforce, HubSpot, SAP, Oracle ERP, ServiceNow, and custom internal databases. Agents can query, read, write, and trigger actions across your full tech stack — autonomously and within defined guardrails.
Most implementations achieve break-even within 4–7 months. LLM-powered document processing typically delivers 10x throughput in the first 90 days. Agentic workflows commonly reduce manual processing time by 60–70%, and our clients report an average 3.2x ROI within 18 months of full deployment.
Both. We're model-agnostic and select the right option based on your requirements around cost, latency, privacy, and accuracy. We regularly work with GPT-4o, Claude 3.5, Gemini 1.5, Llama 3.x, Mistral, Phi-3, and Qwen. For air-gapped or regulated environments, we default to on-premise open-source deployments.
A focused LLM integration (e.g., internal Q&A bot over company documents) typically takes 3–6 weeks. A custom fine-tuned model with RAG and production MLOps can take 8–14 weeks. Full agentic system builds with enterprise integrations are typically 3–5 months. We deliver in iterative sprints with a working prototype in the first 2 weeks.
Absolutely. We offer AI audits covering hallucination rates, RAG pipeline accuracy (using RAGAS), inference cost optimization, prompt engineering review, and security red-teaming. Many clients come to us after an initial internal AI project fails in production — we diagnose, fix, and scale it.
Free Strategic Asset
The 2024 Enterprise LLM Implementation Playbook
Download our comprehensive guide on building production-grade LLM systems — covering model selection, fine-tuning strategies, RAG architectures, and MLOps best practices.
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